Definition of AI
The current assignment is being undertaken for understanding the implications of using artificial intelligence in business operations and processes. The assignment deals with the definition of Artificial Intelligence and also highlight the recent developments in the field of Logistic based AI solutions. The discussion of the types and applications of AI, the assignment will focus on developing the logistics business for the B2B Logistics. This assessment will also take into consideration the advantages as well as disadvantages of using AI solutions. The social, ethical and legal aspects of AI implementation will be also discussed. Recommendations will be suggested based on the evaluation of the discussions made in the other sections which deal with redressal of the current set of issues plaguing the B2B Logistics.
This assignment will be designed from the viewpoint of B2B Logistics’ head of Information and Communication Technology. The B2B Logistics is an old organisation and employs nearly 200 staff members. The base of operation is mostly within Australia and in some parts of Oceania regions. The company deals with end to end logistics solutions for several other companies which are in another sector such as warehousing, mining as well as manufacturing. The increasing demand of the clients for AI-based logistics solutions, the administration of B2B Logistics has decided to expand its business plan for including the services and solutions based on artificial intelligence.
One assumption regarding the case scenario that can be made is that the company has a very dilapidated system for interacting with customers for order procurement as well as providing them with invoices, bills or tracking systems which are critical to reliability and worthiness of the company. Another assumption that can be made is that the company is unable to handle large volume of cargo in an efficient manner.
Definition of AI
Artificial Intelligence can be defined as an intelligence that is displayed by machines wherein the learning as well as action-based capabilities ensure autonomy in comparison to the process-oriented intelligence (Gunasekaran and Ngai, 2014, p.1). Artificial Intelligence can be categorised into augmentation and automation. In the augmentation category, artificial intelligence assists the human counterpart with their daily activities without any autonomy over the output and is normally used in data analysis, Software Solutions and a virtual assistant in order to reduce human bias error.
However, in the automation category, the artificial intelligence has complete autonomy in any field and does not require human intervention. According to Timm, Woelk, Knirsch, Tönshoff & Herzog (2016, p.208), Artificial intelligence has the capacity to undertake intelligent decision making on the basis of the data available. Artificial Intelligence also has the potential for making correct decisions on basis of the previously undertaken decisions and the subsequently collected sets of data.
AI Development in the logistics domain
The growing digitisation in the world of Logistics has enabled several organisations to include artificial intelligence in their supply chains for maximizing the resource management and also effectively reducing time and finances on the operations of supply chain processes. These operations include refilling the inventory, identifying the appropriate mode of transportation, dealing with obstacles to name a few (Ng, 2016, p.9). Automation companies such as WorkFusion have been able to develop a smart set of algorithms which can consider a wide range of variables and utilise the same for providing logistical solutions.
AI Development in the logistics domain
Technological advancement and an increase in demand frequency have forced the Logistic companies and businesses to explore the option of Artificial Intelligence and their ability to provide solutions to the Logistic teams. According to Lam, Choy, Ho, Cheng & Lee (2015, p.777), the most common Areas where artificial intelligence is utilised is the management of resources, risk mitigation, reducing redundancy and cost reduction. Artificial intelligence is also very useful in supporting the traditional techniques of forecasting, enhancing delivery speed by optimisation of routes as well as improved customer services. In order to seamlessly arrange the IT system and enhance data analysis processes bolstering the logistical operations require intelligent automation business.
The shipment price prediction is a very tricky business as the shipping cost depends on a lot of factors such as season, Time of Day and even climate. Artificial Intelligence can easily monitor the mentioned conditions for identifying the right price on the basis of delivery time and the appropriate route for reaching the destination (Abdul Rahman, Shamsuddin, Hassan & Abu Haris, 2016, p.79). The algorithms in the artificial Intelligence can help in monitoring various parameters such as the social and economic challenges, traffic as well as weather which is critical for logistics companies to set up the fair price that is acceptable by both the parties involved in the transaction.
Optimisation of inventory is also one of the reasons why businesses have been investing in artificial intelligence. In the words of Tredinnick (2017, p.38), AI algorithms help in democratization as well as information accessibility that helps in offering a fair price that is acceptable to both the parties. They also monitor the inventory as well as the load capacity which minimises the chances of error that can be faced during the execution of delivery. Artificial intelligence is also responsible for securing as well as managing the inventory of the supplier along with the mode of transportation available for delivering the products. The technology provides data analysis which can be used for learning the current status of the delivery.
In Logistic business, it is always helpful if one makes room for unexpected events that can affect the logistics operations due to a series of circumstances. The occurrence of natural disasters such as floods, earthquakes as well as hurricane, the bankruptcy of carrier agencies and strikes by employee Union are known to affect the course of the logistic flow of work (Chang, Prior & Gottwalt, 2016, p.105). Artificial Intelligence can be successfully trained to consider the contingency plans which allows corrective actions to be taken in time in future in case there is the occurrence of emergency or disruption in workflow. This technology can be used for rerouting the transportation facilities to other distribution centres if the primary one is susceptible to any of the unexpected events.
Types of applications using AI
AI has become synonymous due to companies such as Google and Tesla because of their growing investment in self-driven cars. It has been forecasted that the implementation of self-driven cars will significantly reduce the chances of accidents and also increase the efficiency of ride sharing. The application of AI is just not limited to a few companies but can be also observed in day to day transportation mechanisms like the traffic signals. Automated traffic signals can reduce the delay time and the overall time in travel (Wu, Olson & Dolgui, 2017, p.433). AI is just not limited to the ground but also extends to skies. Commercial flight carriers have been experimenting with AI autopilots for a long time and it has been observed that the average human interaction in cockpit mainly revolves around flight take-off and landing.
Prediction of Shipment Prices using AI
AI has been used in streamlining manufacturing processes. According to Pfahringer and Renz (2015), AI is very useful in processing a vast amount of data in a matter of few seconds which can be used for providing actionable insights which include failure prediction, benchmarking processes across the production lines. The AI will help in identifying the exact moment of giving inputs in terms of raw materials or process variables. In case of logistics AI can help in minimising any friction in customer experience. The use of automated distribution and packaging systems will allow logistics companies to be compliant with their promises.
AI has been increasingly gaining recognition in the healthcare sector as well. The AI analyses the patient data and can utilise the same for improving the outcome of the treatment process. AI has been growing popular because of its reliability and consistency to comply with the health and safety standards. AI has also found a way in the educational sector with the advent of plagiarism checkers and graders like Turnitin which analyse the assignments submitted by students for any malpractices. This will not only improve the overall quality of research but will also minimise the chance of redundancies caused due to overlapping data (Gromovs and Lammi, 2017, p.12).
AI-based applications for B2B Logistics
The section above highlights the importance of AI and its implications for the growth of businesses. By taking a cue from the same, some suggestions can be drawn out to identify the AI-based applications which can be implemented in B2B Logistics. For the sake of faster and efficient packaging and sorting of goods, the B2B Logistics can make use of robots for automation (Kern-Isberner, Fürnkranz & Thimm, 2017). These automated beings will be programmed suitably for packaging and sorting the goods and products on the basis of the delivery requirements as well as customer preferences.
There is also an issue of Travelling Salesman Problem in which the shortest route necessary by a salesman is identified on the basis of the location list. In layman terms, this is the identification of the most efficient route for package delivery. The problem may be simple, but it includes a lot of parameters which need to be considered for deciding on the shortest route (Priestley, 2018). This complemented by the growing consumer demand, managing package delivery becomes a tedious process. The AI can help in analysing the wide range of parameters such as schedules, real-time traffic to name a few and can allocate the job to an executive who will use the interpreted data to enhance delivery efficiency. Hence, B2B Logistics can use AI to identify the quickest route for product delivery and enhance customer satisfaction.
According to Venkatapathy, Bayhan, Zeidlerand & ten Hompel (2017, p.1066), AI can be also utilised for detecting security intrusions and deterring the same. They can reduce the time involved by management by automating the processes, determine brand affinity, anticipate customer purchase, gauge internal compliance, call distribution as well as marketing. Chatbots have become a rage in business models as they can be useful for operational procurement. They can not only interact with suppliers but also set purchase requests, easing up the documentation process. Hence, B2B Logistics can benefit hugely by automating its warehousing and sorting facilities, using AI algorithms for finding most efficient delivery routes and options as well as in customer service as well (Kaplan, 2015).
Optimization of Inventory using AI
Risks and advantages
There are certain aspects of implementing AI that should be considered beforehand. This section will discuss the risks as well as advantages of implementing AI in the B2B Logistics. The primary risk of implementing artificial intelligence is that it will automate operations that require human labour and intelligence. Operations which are highly predictable and repetitive in nature will be replaced by automation and this means that a large part of B2B Logistics may risk downsizing (Mahamuni, 2018). The low and medium-skilled human labour will cease to be efficient and thus automation will define several paradigms of the modern work culture. However, automation is a costly process and requires severe overhauling of the existing infrastructure.
There is also a risk that AI is susceptible to hacking and phishing activities which can lead to the decline of B2B Logistics. This means that with increased dependency on AI, the company will be exposed to malicious activities which in turn will hamper the expansion plans and minimise the reliability and trustworthiness of B2B Logistics among its clientele. This requires a detailed contingency plan which can effectively tackle such incidents.
However, according to Aguezzoul (2014, p.75), the benefit of using AI is that it will complement the performance by individuals. The human element of the company will start showing greater output and precision as they will not have to deal with repetitive and monotonous jobs which will enable in streamlining the goals and objectives of the organisation.
Ethical, legal and social aspects of AI applications
The threat to privacy is a primary ethical issue that can be faced with AI. There is also the issue of threat to human dignity wherein AI cannot possibly perform jobs that require human touch and emotions. The issue of increasing autonomy of machines to collect and utilise data is also considered as a potential ethical issue (Varian, 2018). The chance that AI can interfere and affect human relationships should be also considered. Cybersecurity-related activities and algorithm bias are two other aspects of ethical issues that can originate from the use of AI.
According to Zijm & Klumpp (2017, p.377), AI may inadvertently pose a challenge for the intellectual property laws. The AI in development also collect personal information and in case the information is divulged, the privacy act will become null and void. In such a case it is difficult to identify the real culprit for the misdeed. Breach of data servers by AI bots will cause severe legal repercussions.
Implementation of AI will only increase the issue of unemployment and with no sign of population slowing down, this may be one of the biggest social issues that AI may face. Automation of the criminal justice system may sound innovative and efficient, but there still exists the issue of privacy. Governments can use AI for tapping phones, issue warrant based on online history which are very severe social issues (Nadimpalli, 2017). The concept of AI even though has matured, but the policies and laws that safeguard humanity from the social, legal and ethical aspects of AI are still at a nascent stage and require further debate and discussion.
Social, Ethical, and Legal Aspects of AI Implementation
Recommendation 1
Use of robots and automation for the packaging and sorting facility. The recommendation is specific because the AI will be used to increase the efficiency in packaging, sorting and warehousing. It is measurable as the number of operation related errors can be measured. It is achievable because B2B logistics plans on expanding and hence can be expected to do a heavy investment. It is realistic as with growing consumer demand, it is necessary to maximise efficiency and minimise cost. The automation process is time oriented as it can be completed within the specified time frame.
Recommendation 2
Use of AI to identify the best-suited delivery routes. This recommendation is specific because the AI will consider the various parameters such as traffic, weather and load capacity of a delivery unit before dispatch. It is measurable as the cost of delivery operations will minimise with best-suited route selection. The recommendation is achievable as computers are effectively used in every aspect of doing business and investing in AI will boost the ease of doing business. The recommendation is realistic as a growing number of logistics companies have already started using AI for route selection. The recommendation can be carried out at a specific time frame.
Recommendation 3
Use of Chatbox to perform repetitive tasks such as order procurement, invoicing, billing et cetra. The recommendation is specific as it is aimed towards improving the customer service and minimise the time invested in the repetitive processes. With increasing customer volume, the recommendation’s effectiveness can be measured. Since introducing AI chat box is not cost intensive, hence it is achievable. The competitors and organisations in other industries have already started using AI chat boxes, hence the recommendation is realistic. The chat boxes can be easily integrated into the business operations and thus, is time oriented.
Recommendation 4
B2B Logistics can implement AI based real time information portals in their carriers. The recommendation is specific because these portals can alert the drivers of the big rigs of natural disasters, accidents as well as excessive traffic. The recommendation is measurable as the company will be able to see decline in accidents and late deliveries. There already exist satellite navigation systems which achievable. The ability of the recommendation to minimise loses makes it reliable. AI based apps can be easily installed in Android/IOS phones that the rig drivers use which means that the recommendation can be achieved in a timely manner.
Recommendation 5
For better management of the logistic carriers operated by B2B Logistics, each carrier should be tagged with a signal beacon that can be tracked using AI. This recommendation is highly specific as the using AI assisted logistics softwares, the comapny can increase their operational efficiency while handling a huge volume of work. The human element in the work will be assisted by the AI component which will bring down the freuquency of management error and hence the recommendation is measurable. Commercial signalling beacons and logistics management AI software are easily available and hence achievable. The carriers can be tracked by the clients which will increase reliabilty and trustworthiness. This is not a cost intensive process and can be timely implemented. However the duration of implementation will depend on the size of the carrier fleet.
Recommendations for B2B Logistics
Conclusion
The present assignment is a report prepared in the context of B2B Logistics. The assignment helps in identifying and understanding the implications of artificial intelligence use in business processes as well as operations. The report began with an in-depth description of AI and the categories into which it has been divided. Defining AI is subsequently followed by identifying the recent developments and advancements in AI technology in the context of the logistics industry.
It was observed that the logistics industry has begun embracing the technology partially due to increases in customer demand and partially because of ease of operation. Once the recent developments were identified, the report proceeded with identifying different AI based real-life applications that have been implemented in a variety of industries ranging from transportation, manufacturing processes, customer satisfaction, health care.
This overview helped in proposing the AI-based applications which would be suitable for B2B Logistics. The three applications identified were automation, efficient delivery route identification and customer service. The report also identifies the pros and cons of using AI in general context as well as in the context of B2B Logistics. The legal, social and ethical aspects of AI have been briefly discussed and it was observed that there are several obstacles that are needed to be overcome to minimise the risks and fear associated with AI. Based on the applications selected, SMART recommendations were drawn out which justified the implementation of the applications as specific, measurable, achievable, realistic and time oriented.
References
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